Field of the Invention
Present invention embodiments are related to systems, methods and computer program products for determining a match-cohort or overlap of data pertaining to entities associated with data items stored in multiple entity systems without exposing any information regarding the associated entities, securely exchanging sensitive data related only to the determined match-cohort, and coordinating, among the multiple entity systems, communications with the associated entities based on a stored set of rules.
Discussion of Related Art
Healthcare entities store different components of patient data. Some healthcare entities desire to offer enhanced services by merging or analyzing their patient data with patient data from one or more other healthcare entities. Each healthcare entity serves a patient population (cohort) and some portion of the patient populations may be served by multiple healthcare entities. This portion of the patient populations that is identified as common between at least two healthcare entities is called a match-cohort. As an example, a pharmacy benefit manager (PBM) manages medication data of 400,000 patients and a healthcare provider manages medical data and office visit data of 40,000 patients. The size of an overlapping population (i.e., a match-cohort) is unknown and, in existing healthcare entity systems, can only be determined by exchanging protected healthcare information (PHI).
In order to find the match-cohort with minimal exposure of the PHI, a healthcare entity system of the healthcare provider, which has information regarding a smaller number of patients than a healthcare entity system of the PBM, shares certain patient information such as, for example, date of birth, social security number, last name, first name and gender, with the healthcare entity system of the PBM. In this way, existing healthcare entity systems expose only a minimal amount of the PHI. Before any patient information is shared, the healthcare provider and the PBM typically enter into one or more written agreements to permit the sharing of data and comply with regulations involving the secure handling of PHI. These types of agreements can include a confidentiality agreement, a data sharing agreement, a business associate agreement (BAA), a data security agreement, and/or a collaboration agreement. In addition, most healthcare entities require a healthcare entity receiving PHI to complete a data security review such that the healthcare entity receiving the PHI must provide evidence that their healthcare entity system meets minimum security requirements.
After the healthcare entity system of the PBM receives the certain patient information from the healthcare entity system of the healthcare provider, the healthcare entity system of the PBM compiles a master list of, for example, 25,000 patients in the match-cohort population using master data management technologies.
Another known method for two entities to create a match-cohort is for the two entities to use services of an independent third entity who would receive the certain medical data from the entity systems of the two entities. However, multiple agreements are to be agreed to among the three entities and several security reviews are to be conducted before any sensitive data is shared.
As patient populations served by the different healthcare entities change due to patient drop outs and new patient onboarding, the process described above for determining a match-cohort is repeated periodically.
At times, a healthcare entity that shares match-cohort patient data with one or more other healthcare entities may wish to communicate with a match-cohort patient to positively affect patient behavior and/or prevent over-communication. The healthcare entities may wish to coordinate their communications with the match-cohort patient to positively affect patient behavior and/or prevent over-communication. Effective communication is dependent on knowing the communication history of all of the healthcare entities with the patient. Existing healthcare entity systems do not provide a way to coordinate activities, such as communications, with match-cohort patients.
A blockchain database is a type of distributed ledger, which has a network of replicated databases, each of which is synchronized and visible to anyone within the network. Blockchain networks can be either private or public. Carried out digital transactions are grouped together by blockchain in a cryptographically protected block with other transactions that have occurred and were sent out to the entire network within a preceding 10 minutes. Members in the network with high levels of computing power (miners) compete with each other to validate the transactions by solving complex coded problems. A first miner to solve the problems and validate the block receives a reward. The validated block of transactions is timestamped and added to a chain of blocks in a linear, chronological order. Newly validated blocks are linked to older blocks. The chain of transactions show every transaction performed in a history of the chain. This is a blockchain transaction ledger, which is also known as a blockchain database. The transaction ledger is continually updated and synchronized with each replicated database so that every distributed transaction ledger is identical.
U.S. Patent Application Publication No. 2015/0332283 to Witchey discloses healthcare transaction validation systems and methods. Witchey teaches one or more healthcare validation devices receiving a healthcare transaction that includes a set of healthcare tokens representing actions taken with respect to a stakeholder. A validation device determines whether the healthcare transaction satisfies validity requirements, and when the validity requirements are satisfied, the validation device updates a healthcare historical blockchain.